Algorithms to Live By: The Computer Science of Human Decisions

by Brian Christian

I firmly believe that the important things about humans are social in character and that relief by machines from many of our present demanding intellectual functions will finally give the human race time and incentive to learn how to live well together.

Even the best strategy sometimes yields bad results—which is why computer scientists take care to distinguish between “process” and “outcome.” If you followed the best possible process, then you’ve done all you can, and you shouldn’t blame yourself if things didn’t go your way.

If you wind up stuck in an intractable scenario, remember that heuristics, approximations, and strategic use of randomness can help you find workable solutions. A theme that came up again and again in our interviews with computer scientists was: sometimes “good enough” really is good enough.

Our interviewees were on average more likely to be available when we requested a meeting, say, “next Tuesday between 1:00 and 2:00 p.m. PST” than “at a convenient time this coming week.”

It was seemingly less difficult for them to accommodate our preferences and constraints than to compute a better option based on their own. Computer scientists would nod knowingly here, citing the complexity gap between “verification” and “search”—which is about as wide as the gap between knowing a good song when you hear it and writing one on the spot.

When we interact with other people, we present them with computational problems—not just explicit requests and demands, but implicit challenges such as interpreting our intentions, our beliefs, and our preferences. It stands to reason, therefore, that a computational understanding of such problems casts light on the nature of human interaction. We can be “computationally kind” to others by framing issues in terms that make the underlying computational problem easier.

Alternatively, you can try to reduce, rather than maximize, the number of options that you give other people—say, offering a choice between two or three restaurants rather than ten. If each person in the group eliminates their least preferred option, that makes the task easier for everyone. And if you’re inviting somebody out to lunch, or scheduling a meeting, offering one or two concrete proposals that they can accept or decline is a good starting point.

Some parking garages are structured this way, with a single helix winding upward from the ground level. Their computational load is zero: one simply drives forward until the first space appears, then takes it. Whatever the other possible factors for and against this kind of construction, we can definitely say that it’s cognitively humane to its drivers—computationally kind.

As a parallel example, consider the computational problem posed by a bus stop. If there is a live display saying that the next bus is “arriving in 10 minutes,” then you get to decide once whether to wait, rather than taking the bus’s continued not-coming as a stream of inferential evidence, moment by moment, and having to redecide and redecide. Moreover, you can take your attention away from squinting down the road—spinning—for those ten minutes straight.

Up against such hard cases, effective algorithms make assumptions, show a bias toward simpler solutions, trade off the costs of error against the costs of delay, and take chances. These aren’t the concessions we make when we can’t be rational. They’re what being rational means.